Best Practices From Financial Institutions Using Predictive Analytics

Thursday, April 14, 2022  |  Becky Summers

Financial institutions that are successful at using data can reap the benefits of more engaged accountholders to drive greater distinction from competitors. Data must be connected and used within the organization to answer strategic questions. In addition to being actionable, the data must be scalable and predictive to handle larger and complex data sets. Organizations must be connected and use predictive and timely data to be effective in deepening relationships and building trust.


A connected organization is one that leverages data throughout the organization. These organizations have a common understanding of the data governance and data sources to ask strategic questions that are answered by this connected data. This requires data from various systems to empower users with data to create strategies, make decisions and create accountholder-centric targeting and financial support.

But data alone does not solve complex business problems or strategic questions without context. Data must be used in a way that is predictive, timely and relevant.

Consumers are clearly telling a story based on how they are spending money each day. While financial institutions see this data running through a multitude of systems, it is often difficult to understand and use. On top of that, the story may change or evolve constantly.

To be effective in using data, financial institutions should:

  • Make sure data is cleansed, categorized and usable
  • Develop strategic questions to answer with data
  • Use the data to develop and execute plans

Cleansed Data

Begin by cleansing the data in a way that makes it actionable.Cleansing is taking all those combinations of confusing and unusable data strings (or dirty strings as they say) so that the data can be understood.

For example, you may need to know that a cryptic series of letters and numbers is in a Chase transaction. But, even more important, you need to know based on the remaining data in the string if it is a credit card payment versus a mortgage payment.

This is a bit of art and science. Organizations are using data librarians and data scientists to decrypt these data strings and keep current as organizations evolve and others enter the market. Many financial institutions are partnering with third-party experts to improve the speed and accuracy of getting data cleansed so they can move to market much faster with strategies.

Consider these valuable cleansed data best practices:

Held Elsewhere Accounts

Your accountholders are paying other credit, mortgage and loan companies from your checking accounts. You can make the most of this data to improve your offerings.

Use microtargeting to identify small groups of accountholders paying with credit cards. As you target market using payments to a specific financial institution, consider your value proposition and lead with that specific topic. This helps personalize the offer. Examples include better payment terms, saving money and even lower fees. Lead with your best foot!

Consider whether you need to do a product gap analysis. How do your products stack up against the competition? Are larger groups of accountholders using a particular competitor? If so, why? You may be able to make a basic product feature comparison to understand why. But accountholder-centric organizations often take the next step and do a survey to determine specific brand, product or even feature gaps to redesign the product or market differently. Focus on changes that your accountholders consider most important to help deepen relationships with those accountholders.

Utilization Campaigns

Financial Institutions find great success in cross-selling debit, credit card and home equity lines of credit into households. But using cleansed data will help you identify utilization campaign opportunities.

Limited usage accountholders should be encouraged to use products. When you can, personalize the messages with ideas for use. For example, if the accountholder has been shopping at a home improvement store, feature home improvements in your ads.

Consider recommending your cardholders use credit or debit cards for subscriptions. Subscriptions are recurring payments for services that are often paid through a recurring charge. Encouraging use of your card helps drive usage, improve interchange income and create stickiness for the relationship.

Categorized Data

Categorizing data simply means creating categories to make it more usable. With categorized data, financial institutions can develop data models that support accountholder needs, marketing efforts and attrition defense tactics.

To be predictive, data points must be used in concert with each other, and categorization helps do just that. Actionable data should be based on timeliness, frequency and spend. Use knowledge to personalize offers so they are noticed and help improve results. Be more “Amazon-like” with behavioral marketing. For example, do you have accountholders getting married? Newlyweds have a variety of financial needs. How do you best identify them?

Categorical purchases that represent a potential wedding:

  • A new subscription being paid from the checking account to wedding planning subscription websites
  • Jewelry store recurring payments
  • Resort purchases
  • Venue rentals

Potential needs and things to watch for:

  • Loan for wedding costs
  • Financial literacy with goal setting and savings plans
  • Guarding against attrition (Will the newlyweds want to combine accounts?)

Data categorization can lead to personalized marketing and the ability to meet the needs of accountholders, deepening their relationship with your financial institution. But with the above example, you can also see the need to make offers timely and relevant. If the accountholder subscribed to a wedding planning website a year ago, it’s likely their needs have already shifted. Maybe it’s time for a mortgage loan? Let your data lead you so you are on target!

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